In this project we will work on (at least) three research projects:
1. OpenFOAM for hydropower applications
2. Turbulence-resolving simulations of flows over basic airframe or engine configurations
3. Prediction of aeroacoustic noise from trucks
4. Multiphase flow
Today, the traditional way to work with aerodynamic design is challenged by tougher requirements on through put times, cost efficiency and rise in product quality. The trend is going towards more automatized processes where simulations and optimization techniques in aerodynamic design (aerodynamic shape optimization) is getting more and more important in the design process. It yields that the algorithms which are used in the numerical simulations have to be efficient and give accurate results in a large part of the flight envelope. This should apply for the early stages of conceptual design as well as for detailed product design.
Most of the flow simulations made today assumes steady-state conditions, i.e. the flow field is not time dependent. However, most of the problems which are analyzed are unsteady problems which yields that time-accurate simulations are needed. Two important examples of such problems are flows in highly bent air inlets and buffeting effects on aircraft wings, i.e. an unsteady flow phenomenon which develops at transonic speeds. Today's unsteady methods, applied on industrial problems, are not sufficiently efficient and accurate to be used in the practical engineer work. Thus, improvements have to be made if a more time and cost efficient model based product development process shall be used.
We will carry out turbulence-resolving simulations using hybrid LES-RANS modelling approaches for the flows around the tandem cylinders and the wing mock-up. The noise generation will be studied in connection to resolved local turbulent flow properties, as well as to temporal and spatial correlations for Innovative methodologies and technologies for reducing a aircraft noise generation and emission noise-source formulation. If needed, the turbulence-resolving modelling approach will be further improved in order that important flow properties, and thus inherent potent noise generation, are well resolved in a reasonably wide span of frequencies. For CAA analysis we will invoke a two-step hybrid method, where turbulence-resolving simulations are first conducted for the formulation of noise source which is then coupled to acoustic analogies of different types, e.g., the Curle, the Kirchhoff and the FH-W method. For both efficient and accurate analyses, the impact of near-field flow resolution enclosed by the integral surface, as well as the location of the integral surface, will be verified in the analysis using the Kirchhoff and the FH-W method.
Wind noise is the dominating contributor to the in-cab noise level for trucks and buses driving at cruise speed (80-90 kph). A quiet driver environment is a contributing sales motivation in the sense of comfort, safety, and quality. Wind noise is almost always undesired. Over time the engine noise has been reduced and the relative wind noise contribution has increased. Wind noise will become even more pronounced in the future with hybrid or full electric propulsion.
In product development, a predictive numerical method for wind noise is needed. For engine noise there are well established tools and methods available and in use at Volvo today. However, when it comes to wind noise, many areas of CAA (Computational AeroAcoustics) are still active research areas, meaning that methods and
tools are not as established and mature as when it comes to e.g. aerodynamics.
This project aims to establish a fundamental understanding about how noise sources are generated in the external flow field around trucks and buses and how the noise sources depend on geometrical complexity and flow speed.
4. In this project we study multiphase flow phenomena. The applications are ``Clean and flexible use of new difficult biomass fuels in small to medium-scale combustion'' and ``Thermal control of a lab-scale in-situ reactor for soot oxidation''. We are using Fluent.